We use an independent decomposed attribute encoder to map the source image to shared domain and then apply reshape operation to get \(\bar{f}_s(j) \in \mathbb {R}^{(hw) \times c}\) .
Here we refer to the method of [1]} to establish the correspondence between the target pose and the source image. But unlike [1]}, which establishes the one-to-one correspondence between the semantic map and the exemplar image by selecting the largest value in each row of correspondence matrix. We think that duo to the largely deformation of pose, there may be a one-to-many correspondence between the target pose and the source image, that is to say, there is a correspondence between a point on the target image and a point on the source image and its neighborhood. It's benefit if we can also establish this relationship, so we save the complete correspondence matrix. In addition, the other difference is that [1]} uses bilinear interpolation to downsample the source image to a specific size, whereas we use an independent decomposed attribute encoder to map the source image to specific size. In fact, the establishment of correspondence and the use of decomposed attribute encoder play a mutually promoting role: the establishment of correspondence can solve the problem of misalignment between the source image and the target image, so that the network can generate more realistic images, on the other hand, the use of decomposed attribute encoder can better extract image features to establish a more accurate dense correspondence.
